Insights into modifiers effects in differential mobility spectrometry: A data science approach for metabolomics and peptidomics.

Autor: Stepanovic S; Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Geneva, Switzerland.; Institute of Chemistry, Technology and Metallurgy, University of Belgrade, Belgrade, Serbia., Ekmekciu L; Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Geneva, Switzerland., Alghanem B; Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Geneva, Switzerland.; Medical Research Core Facility and Platforms (MRCFP), King Abdullah International Medical Research Center, Riyadh, Saudi Arabia., Hopfgartner G; Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Geneva, Switzerland.
Jazyk: angličtina
Zdroj: Journal of mass spectrometry : JMS [J Mass Spectrom] 2024 Jun; Vol. 59 (6), pp. e5039.
DOI: 10.1002/jms.5039
Abstrakt: Utilizing a data-driven approach, this study investigates modifier effects on compensation voltage in differential mobility spectrometry-mass spectrometry (DMS-MS) for metabolites and peptides. Our analysis uncovers specific factors causing signal suppression in small molecules and pinpoints both signal suppression mechanisms and the analytes involved. In peptides, machine learning models discern a relationship between molecular weight, topological polar surface area, peptide charge, and proton transfer-induced signal suppression. The models exhibit robust performance, offering valuable insights for the application of DMS to metabolites and tryptic peptides analysis by DMS-MS.
(© 2024 The Authors. Journal of Mass Spectrometry published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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